Innovative Data Science Approaches to Identify Individuals, Populations, and Communities at High Risk for Suicide Proceedings of a Workshop (2022) / Chapter Skim
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Workshop Overview
Workshop Overview
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(U.S. Department of Health and Human Services Office of the Surgeon General and National Action Alliance for Suicide Prevention, 2012)
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SOURCE: U.S. Department of Health and Human Services Office of the Surgeon General and National Action Alliance for Suicide Prevention.
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Thus, the ability to leverage real-time data sources and novel methodologies to identify and mitigate suicide risk needs to be balanced with minimizing unintended consequences. This challenge was the impetus for the recent Workshop on Innovative Data Science Approaches to Assess Suicide Risk in Individuals, Populations, and Communities: Current Practices, Opportunities.
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The presentations and discussions are organized thematically, rather than strictly chronologically, to allow for similar points made by different participants to be synthesized and streamlined. The proceedings highlights individual participants' suggestions to help advance innovative data science techniques including AI/ML learning to help inform upstream suicide prevention efforts at the individual, community, and population levels.
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. BOX 2 Suggestions from Individual Workshop Participants to Advance Innovative Data Science Techniques to Help Inform Upstream Suicide Prevention Efforts at the Individual, Community, and Population Levels Improving Online Data Collection and Analysis for Suicide Prevention • Explore AI/ML and natural-language processing techniques to help improve the support available on social media platforms for those at risk for suicide.
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• Incorporate into suicide prevention efforts the perspectives of those with lived experiences. (Goldfinger, Reece, Resnik, Swanson, Whiteside)
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(Reece) Advancing Public Policy • Create incentives to improve communication among those involved in suicide risk detection and response.
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A second action would be the detection of hotspots for a communitylevel intervention; one might, for instance, find that students in a particular high school are at high risk for suicidal behavior. Third, data science tools could be used for detection at the individual level -- say, of a student who is at the present moment experiencing suicidal ideation or a mental health crisis.
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Researchers can then train machine learning algorithms on those records to identify patterns and pick out words or phrases, including diagnoses or medications, that are more commonly associated with suicide than other words or phrases. They can then deploy the algorithms to find those data points in new patient cohorts and predict suicide risk in those populations.
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Confrontations with first responders can actually increase the risk of suicide, which means that these systems can sometimes do more harm than good, Marks said. Medical suicide prediction carries certain risks as well -- such as the possibility of stigmatizing someone who has contemplated suicide -- but the risks are more concerning for social suicide prediction, Marks said, due to the lack of transparency and the fact that less is known about social suicide prediction methods (Barnett and Torous, 2019; Marks, 2019)
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For instance, if there are no safe and effective interventions that can be triggered once suicide risk is identified, the prediction algorithm should not be used until the treatment shortcomings are addressed. In the case of social suicide prediction, he said consumer consent should be sought and individuals should be allowed to opt in or opt out of the prediction systems employed by social media companies.
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Allen said that "this presents us with a situation where we clearly need to come up with models that adapt to these rapidly changing landscapes, both in terms of the epidemiology of mental health difficulties as well as the affordances of consumer behavior and consumer devices." Against this backdrop, Allen said, it is important to understand the strengths and weaknesses of traditional health research approaches versus business innovation approaches. The traditional health research approach emphasizes internal validity and, excluding alternative explanations, has a slow funding and research cycle, assumes a static environment in terms of the risk factors, and uses rigorous measures and methods.
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. Allen said that in pursuing these core business models, many technology businesses collect large amounts of the types of data that could be used for suicide predictions.
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She said that current approaches to predicting and preventing suicide often rely on clinical interviews, patient self-reports, family observations, and assessment scales, but these approaches are limited by the fact that they do not allow for frequent monitoring of risk for suicidality. The data are not collected frequently enough to monitor for important changes in people.
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De Choudhury cautioned that not everybody is on social media, and thus social media needs to be thought of as part of a broader data ecology and to be applied in the context of other conventional data sources. She emphasized that 9 See https://cesd-r.com/ (accessed August 22, 2022)
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Working with Social Media Data to Predict Suicide Risk at the Individual and Population Levels Kenton White, co-founder and chief data scientist at Advanced Symbolics, an AI company in Ottawa, Canada, said that suicide, unlike many other health conditions, is almost entirely preventable yet many lives are lost every year by death from suicide. He listed three reasons for this.
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MHA's mission is driven by its commitment to promote mental health as a critical part of overall wellness, including prevention services for all; early identification and intervention for those at risk; and integrated care, services, and supports for those who need them, with recovery as the goal. Nguyen described a program, launched in 2014, called MHA Screening that provides a variety of online mental health screening tests for depression, anxiety, bipolar disorder, posttraumatic stress disorder, alcohol and substance use disorders, psychosis, eating disorders, and postpartum depression.a By offering these tests online, MHA makes screening accessible to many people who might otherwise not get screened for mental health issues.
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The company is looking for opportunities to create free digital tools and products that can help fill the gaps in mental health coverage and help people who might not have easy access to the mental health care system." a See https://screening.mhanational.org/about-our-screening-tools/ (accessed August 23, 2022)
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White noted that the nationwide project unfortunately did not launch because of high concerns about the potential misuse of data, such as the Facebook–Cambridge Analytica data scandal in which a data analytics company harvested data from millions of Facebook users to influence America voters.12 White said that his team has learned valuable lessons from the experience, however. The first is that any such research really has to be done "from a position of trust." With that in mind, the team is now working with Indigenous communities in northern Canada, through partners who have established trust with the communities by working with them over many decades on mental 11 See https://www.mobihealthnews.com/content/canada-will-use-ai-monitor-suicidal social-media-posts (accessed August 29, 2022)
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Coppersmith suggested three guiding assumptions when working with social media data to predict suicide risk. The first is that technology companies have -- or could have -- the ability to identify at least some of the people at risk for suicide based on their behavior.
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This raises the question of what technology companies can do with this information, such as aggregating it and adjusting it in various ways, so that they can hand off the modified information to mental health professionals for use in suicide prediction and prevention. Coppersmith suggested that one approach could be by grouping data from a number of individuals to make it possible to address many of the privacy concerns while still providing useful information.
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See https://trends.google.com/trends/? geo=US (accessed August 19, 2022)
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The researchers found a correlation between the two sets of numbers, but in some states the official suicide numbers were significantly less than would be expected from the search engine data. The implication is that in these states there is underreporting of suicides because of the stigma.
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. CURRENT OFFLINE APPROACHES TO PREDICTING AND PREVENTING SUICIDE Several speakers spoke about current offline approaches to suicide prevention based on data that can be collected from tools including electronic health records, hospital records, and demographic databases.
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She also reiterated the legal and ethical issues, and wider implications concerning suicide risk detection and interventions in social media platforms (Celedonia et al., 2021)
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Suicide Prediction and Prevention in the Military Health System Dan Evatt, who leads a research team at the Psychological Health Center of Excellence, provided an overview of identification and management of suicide risk in the Military Health System (MHS) and discussed analytic and practical consideration related to implementing suicide analytic screening in the MHS.
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We want to break down those barriers and really what we are doing is directing service members to the resources they need." Alternative Crisis Response John Franklin Sierra, health systems engineer in the Los Angeles County Alternatives to Incarceration Office, described that county's alternative crisis response efforts, which are intended to be used in crisis cases when law enforcement does not need to be involved. One goal of the efforts is to have as many people as possible use the 988 Suicide & Crisis Lifeline18 (formerly known as the National Suicide Prevention Lifeline)
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Level 2 calls require immediate remote support through, for example, a transfer to a suicide prevention hotline. Level 3 calls require in-person support such as what could be provided by emergency medical services (EMS)
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He said that the 988 number, as envisioned, could help provide more equitable access to vital mental health and substance use services and help decrease suicide and mental health stigma. Linking Datasets to Predict Suicide Risk Holly Wilcox, professor in the Department of Psychiatry and Behavioral Sciences and the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health, who serves as co-chair of the Maryland Suicide Prevention Commission, discussed various aspects of suicide prediction and prevention at the state and local levels, with a particular focus on the value of linking multiple datasets to increase predictive power.
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Among the other sources of data provided to the suicide data warehouse are electronic health records, insurance claims data, hospital discharge data, information on deaths and their causes for the Office of the Chief Medical Examiner, Census-derived geographical data, Medicare and Medicaid data, and data from the VA. "We really want to know which of these sources are useful in suicide risk prediction," she said, and also to provide a framework for other states to follow if they wish to use similar predictive analytics and modeling, using commonly available data sources.
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It is looking at other potential sources of data, such as the suicide prevention toolkit of Epic Systems Corporation. Furthermore, Wilcox described a pilot study in which she and her colleagues sent caring text messages to those who had been treated in the health care system for suicide risk and had then returned home.
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The researchers recorded and analyzed the conversations of 30 adolescents who presented to an emergency department for suicide risk and 30 adolescents who were nonsuicidal (as controls)
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She added that it is also possible to use ML to predict which 25 DBT is a type of cognitive behavioral therapy that can be used to treat people with suicidal ideation and various other complex disorders, helping them to develop concrete coping skills. 26 These are comprehensive resources that provide vital tools for implementing DBT skills training.
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Czerwinski concluded that in developing mental health applications, it is important that the design takes the unique emotional, environmental, and personal contexts of individuals at risk for suicide into account and allows for evidence-based support with personalized skill recommendations. CURRENT ONLINE APPROACHES TO PREDICTING AND PREVENTING SUICIDE Several speakers described the current state of the art of online approaches to predicting and preventing suicide and what possibilities the future might hold.
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Also, both websites provide high-quality, expert-informed health information about common mental health conditions through knowledge panels located on the search results page, to make it easier for users to find information on topics including the symptoms and common treatments for depression and anxiety. Bell referred to a set of principles published by the National Academy of Medicine on how to define authoritative health content sources and the ways in which those sources attain and maintain their authority.
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Koko integrates suicide prevention resources directly onto social networks and large online communities to identify risk and triage the appropriate response, from immediate crisis to well-being courses to peer supports. Morris said the toolkit that Koko uses has two components.
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After detecting this search, Koko's program suppressed the search results and instead showed a public service announcement with links. Koko also sent a direct message to that user with KokoBot, which offered various services, such as a connection with peer counselors.
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Peer-to-Peer Mental Health Support Tim Althoff, the director of the Behavioral Data Science Group in the University of Washington's Allen School of Computer Science & Engineering, described a research collaboration with a social media platform called TalkLife,29 which is focused on providing peer support to people with mental health challenges. He discussed how AI/ML and natural-language processing techniques can help improve the support on these platforms.
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Althoff shared data from a randomized controlled trial of one human–AI collaboration approach undertaken with peer supporters on TalkLife, which showed that those who had been given machine-generated suggestions expressed nearly 20 percent more empathy than those who had not
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CONSIDERATIONS FOR MOVING FORWARD A number of speakers discussed specific issues to take into account as developers work to create ways to use online data in predicting and preventing suicide. Moving from Individual Predictions to Population Priorities in Suicide Prevention Philip Resnik, professor in the Department of Linguistics and the Institute for Advanced Computer Studies at the University of Maryland, began his presentation by asking that, given that the mental health care system is currently overburdened and primary care clinicians are at the front lines to help, "What do we do about this overburdening?
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Clinicians are most successful if they take individuals' lived experiences into account, but these are difficult to analyze with ML because each person's lived experiences are unique. He said, "You have to be able to find cross-cutting insights and generalizations if you are going to be able to make effective use of the information about people's lived experiences." He added that "we need a solution that takes the lived experiences into account and also allows us to extract generalizations and useful insights from a population." Resnik's team applied this approach in analyzing more than 16,000 responses on Reddit to a question about what had helped keep formerly suicidal individuals alive when they were contemplating suicide (Resnik et al.,
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. 31 See https://www.pewresearch.org/fact-tank/2019/01/17/where-millennials-end-andgeneration-z-begins/ (accessed August 18, 2022)
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One thing that makes Koko particularly appealing to today's adolescents and young adults, she said, it that it integrates suicide prevention resources directly into social networks and large online communities, identifying risk and triaging the appropriate response from providing immediate crisis help to offering well-being courses and peer supports. Approaches like Koko "really open up opportunities to problem-solve with social platforms around risk assessment and triage," she said, but, importantly, they also open doors to the provision of more upstream preventative interventions.
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. Haritatos urged that it is essential for "AI, ML, and suicide prevention research communities to play a defining role while all of this is still taking shape." Haritatos said that because the members of Gen Z are incredibly savvy about digital spaces, they are well positioned to play major roles in shaping the digital and online technologies that will be increasingly important in their lives.
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The principle of beneficence calls for the benefits of an action to outweigh the harms or risks, which requires careful consideration of both benefits and risks. 37 See https://www.genome.gov/ (accessed August 17, 2022)
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Concerning privacy, for instance, researchers should consider how best to convey to participants which personal data will be collected and how those data will be used and shared. Concerning 38 See https://www.pnas.org/doi/10.1073/pnas.1320040111 (accessed August 17, 2022)
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Designing Systems for Equity Rayid Ghani, professor in the ML Department at Carnegie Mellon University, spoke about practical lessons and challenges in building fair and equitable systems for suicide risk assessment. He said that inequity often results from the fact that most mental health organizations, whether they are government agencies, nongovernmental organizations, or mental health services organizations, generally are operating with very limited resources.
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Regarding how to determine what types of bias to address, Ghani said the data science field has recently been working on different definitions of bias, and it turns out that the definitions are very similar to the ones that philosophers and ethicists have talked about for centuries. Some of the possibilities include seeking to allocate equal resources to each group, allocating resources proportionately to need, making sure that people of a certain group are not missed disproportionately, and making sure that there is not a disproportionate number of false positives from a certain group.
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They have the potential to be equitable, but they have to be designed that way, "and that process has to be an integral part of every project, from scoping the project to engaging the communities that are being affected, to validation and monitoring." He cautioned that equity in data science and AI is a new area with many unknowns and tentative ideas and solutions. "So if you're working in this space, be careful," he said.
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He said that "we have to talk about the business incentives first and foremost to ensure sustainable change in all of our ecosystems that touch upon the lives of those at risk of suicide." He said that community-based organizations that provide mental health and suicide prevention services, including Didi Hirsch, have largely been underfunded. He added that there "should be financial incentives for data sharing, data mining, and de-identified data research." And the third is time, he added, noting that "the time to change can actually be accelerated and we can be more efficient with our time through partnerships." Opportunities for Research on Risk Assessment Richard McKeon, chief of the Suicide Prevention Branch at the Substance Abuse and Mental Health Services Administration (SAMHSA)
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Regarding ongoing care for at-risk individuals, McKeon spoke about the people who can fall through the cracks of suicide risk assessments. He said that the Zero Suicide Model40 involves identifying people who are found to be at an elevated risk of suicide and then providing them with evidence-based 40 See https://zerosuicide.edc.org/ (accessed August 17, 2022)
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Sec 41 See https://mmhpi.org/ (accessed August 23, 2022)
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Research has shown that clinicians are eager to integrate digital tools and data science into their clinical practice, particularly those tools that optimize their time and provide more patient information than they can get from office visits (Ahmed et al., 2020; Chi et al., 2021; Marwaha et al., 2022)
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In particular, he focused on the gaps in data and the challenges in detection and response to suicide risk.
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A third type of detection challenge arises from gaps in communication among those involved in suicide risk detection and response -- the individuals, their families, emergency medical services, mobile crisis clinicians, social media, and so forth -- which is usually challenging and often does not 42 International Classification of Diseases (ICD) is the official system designed to promote international comparability in the collection, processing, classification, and presentation of mortality statistics.
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. 44 Tennessee Suicide Prevention Network is a statewide public–private organization and association of clinical, public health, public safety, clergy, and social actors that provides suicide prevention resources.
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Another common hope was that they would get help and feel better, whether that came about through clinical help, peer support, emergency ser vices, or some other means. Whiteside said that individuals with lived experiences prefer to have options when their conditions are being addressed.
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Thus, he is particularly sensitive to what types of intervention are most appropriate and which are ineffective and may harm an individual's autonomy. Concerning the role that social media can play in assisting those dealing with depression and suicide risk, Swanson said that he has been speaking about suicide prevention for 8 years and many people have approached him with concerns either about themselves or other people, but at no point has anyone ever approached him in person.
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Building on that, Swanson suggested it would be useful to speak with people who have relevant lived experiences to ask them what they think might be the best approach. Reece added that it is the people surrounding an at-risk individual who can provide the sort of connectedness that is so important in suicide prevention.
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Obtaining trust will be important with various groups, particularly young people and various ethnic groups, and any attempts to address suicide risk in these groups will need buy-in and input from the groups, he said.
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That experience should inform every aspect of the work to prevent suicide, and those with lived experiences should be involved in decisions on research and standards of care, he added. Younger generations -- Gen Z in particular -- have a different, more intimate relationship with social media spaces than older generations, and it will be important to engage them in the process of finding solutions to their mental health challenges.
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2019. Ethics, transparency, and public health at the intersec tion of innovation and Facebook's suicide prevention efforts.
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2020. Assessing population-level symptoms of anxiety, depression, and suicide risk in real time using NLP applied to social media data.
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2016. Helping callers to the National Suicide Prevention Lifeline who are at imminent risk of suicide: Evaluation of caller risk profiles and interventions implemented.
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2021. Evaluation of the Recovery Engagement and Coordination for Health -- Veterans Enhanced Treatment suicide risk modeling clinical program in the Veterans Health Administration.
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U.S. Department of Health and Human Services Office of the Surgeon General and National Action Alliance for Suicide Prevention.
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